Automated image analysis

ABSTRACT

One automated imaging process, as described herein, includes: a) obtaining digital images of objects in a biological sample; b) selecting a plurality of objects of interest from the digital images; c) obtaining multiple images of the selected objects of interest at a plurality of different wavelengths; d) combining one of said multiple images with a corresponding digital image to produce a combined image; and e) analyzing the combined image in order to characterize the biological sample.

RELATED APPLICATION DATA

The present application is a continuation of pending U.S. patentapplication Ser. No. 11/120,437, filed May 2, 2005, the priority ofwhich is claimed under 35 U.S.C. §120, and the contents of which isincorporated herein by reference in its entirety, as though set forth infull.

FIELD OF THE SUBJECT MATTER

The subject matter described herein relates to methods and devicesuseful in automated image analysis.

BACKGROUND

Cytological imaging of medical specimens is a tedious but crucial toolfor medical analyses. Automated cytological imagers have been developedto meet the need for more uniform cytological image analyses. Automatedcytological imagers do not vary as greatly in their interpretations ofslides, are less subject to fatigue, and can provide much greaterthroughput as compared to humans.

Several previously developed and some currently available automatedsystems are used in conjunction with additional human analysis, and areused to increase the number of samples assayed and to lessen the fatigueexperienced by the human analyst. Automated screeners can be used toselect from each sample, objects for further human review. This methodcan increase the sensitivity of such assays, as the machine may morereadily and economically identify those objects of interest in eachsample to be analyzed by a human.

However, automated imagers are limited by the sample and data providedto them and by their programming. Additionally, for computationalreasons, imagers typically use monochromatic, black and white images fortheir analyses, whereas the sample itself may provide a great range ofspectral data and other information, particularly for cytologicallystained samples.

For example, in the automated image analysis of pap-stained samples, theclassification of abnormal objects in a conventional automated screeningsystem can be complicated by the presence of normal metaplastic cellsand other confounding objects. Some imaging systems identify cells ofinterest in pap-stained specimens on the basis of their optical density,as they or their nuclei may appear “darker” (more optically dense)and/or larger than do normal cells in the specimen. Metaplastic cells inthe stained specimen also have dark cytoplasms and consequently reducednuclear:cytoplasmic contrast that may contribute to errors inmeasurement. The metaplastic cells can be quite numerous on a slide,while abnormal cells may appear infrequently, and thus automated imagerscan undesirably select the metaplastic cells for human review as theyappear equivalently dark to the imager but are much more numerous thanthe abnormal cells. The false selection rate of the frequently occurringbut disease-negative metaplastic cells by the imager thus limitsaccurate disease detection.

SUMMARY OF THE SUBJECT MATTER

In accordance with one embodiment disclosed herein, an automated imagingprocess includes: a) obtaining digital images of objects in a biologicalsample; b) selecting a plurality of objects of interest from the digitalimages; c) obtaining multiple images of the objects of interest at aplurality of different wavelengths; d) combining one of said multipleimages with a corresponding digital image to produce a combined image;and e) analyzing the combined image in order to characterize thebiological sample.

In accordance with another embodiment disclosed herein, an automatedimaging process includes: a) obtaining digital images of objects in abiological sample; b) selecting at least one object of interest from thedigital images; c) obtaining at least one image of the at least oneobject of interest at a plurality of different wavelengths to form a setof multi-wavelength images; d) analyzing the set of multi-wavelengthimages in order to characterize the biological sample.

In accordance with yet another embodiment disclosed herein, an apparatusfor use in a automated imaging process includes: a) at least one lightsource that can provide at least one spectral region to a sample; b) atleast one detector that can detect at least one set of images ofportions of the sample when illuminated by the at least one spectralregion; and c) at least one computer that can select at least one subsetof the images based on at least one set of criteria. If more than twosets of the images are collected, those images may be combined to format least one combined image. Then at least one computer may also analyzethe sets of images for the selected subset and can select a furthersubset of the sets of images based on a second set of criteria which maybe the first set of criteria or a different set.

In accordance with still another embodiment disclosed herein, anapparatus for use in an automated imaging process includes: a) a firstlight source that can provide a first spectral region to a sample; b) afirst detector that can detect first images of portions of the samplewhen illuminated by the first spectral region; c) a first computer thatcan select a subset of the first images based a first set of criteria;d) a second light source, which may be the first light source or adifferent light source, that can provide a second spectral regiondifferent from the first spectral region; e) a second detector, whichmay be the first detector or a different detector, that can detect asecond image of the images in the selected subset when illuminated bythe second spectral region; and f) a second computer, which may be thefirst computer or a different computer, that can produce a combinedimage comprising the second image and the first image for the selectedsubset and can select a further subset of the combined images based on asecond set of criteria which may be the first set of criteria or adifferent set.

In accordance with a still further embodiment disclosed herein, anapparatus for use in an automated imaging process includes: a) a firstlight source that can provide a first spectral region to a sample; b) afirst detector that can detect first images of portions of the samplewhen illuminated by the first spectral region; c) a first computer thatcan select a subset of the first images based a first set of criteria;d) a second light source, which may be the first light source or adifferent light source, that can provide a second spectral regiondifferent from the first spectral region; e) a second detector, whichmay be the first detector or a different detector, that can detect asecond image of the images in the selected subset when illuminated bythe second spectral region; and f) a second computer, which may be thefirst computer or a different computer, that can analyze the first andsecond images for the selected subset and can select a further subset ofthe first and second images based on second set of criteria which may bethe first set of criteria or a different set.

BRIEF DESCRIPTION OF THE FIGURE

FIG. 1 depicts the incremental nuclear:cytoplasmic contrast levels for adual wavelength (570 nm plus 2nd wavelength) combined image as comparedto a single wavelength (570 nm) image, for 11 measured objects.Circle=normal intermediate cell, x=normal metaplastic cell, dot=abnormalcell.

DETAILED DESCRIPTION

Automated imaging processes and/or devices utilize multiple wavelengthsof light to illuminate the sample and obtain images that can bemanipulated automatically or by an operator, as described herein. Imagesthat contain relevant information can also be obtained at differentwavelengths in order to subject the combined image to additionalanalysis. In addition, objects found in one image can be subjected todifferent wavelengths of light in order to analyze the object in depthbefore rendering a diagnosis based on the sample. In some embodiments,relevant information can be obtained by illuminating the sample orspecimen with white light and placing at least one color filter betweenthe specimen/sample and at least one TV camera or other camera. A camerawith switchable color filters may also be utilized. In some embodiments,an operator of the system may go back to the cell location, if aparticular set of images comprises a “cell of interest”, and produce orretrieve additional images to aid the researcher, computer or technicianin completing the information about the sample or specimen.

Also provided herein are several methods, processes and devices of andfor further investigating a set of objects by an automated imager, whichmethods, processes and devices may be used singly or in combination.Through the use of information obtained by analyzing objects at multiplewavelengths, cells or clusters containing features of interest(“positives”) can be better distinguished from false alarm or negativecells in a selected set. Specific cell types, such as endometrial cellsor endocervical cells, or cells of a certain abnormality, may also beidentified through such interrogation.

A number of discrete imaging systems are commercially available as ofthe time that the application for the present patent was filed,including Cytyc Corporation's THINPREP® Imaging System, the TriPathFOCALPOINT™ Profiler, the ChromaVision ACIS® System, the CompuCyt iCyteImaging System, the Applied Imaging CYTOVISION™ System, and the VeracelVerasys™ Imaging System. It will be appreciated that these apparatus anddevices can be modified to incorporate additional imaging steps, such asthose described herein.

The current THINPREP® Imaging System (“TIS”) identifies fields of viewhaving one or more objects of interest in a specimen sample slide,including both single cells and clusters, stained by a Papanicolaoustaining process and digitally imaged. The TIS can compile a list, forexample, of the 100 single objects on a given sample slide with thehighest integrated optical density and a list of the 20 clusters withthe highest average optical density. Other values of objects andclusters can be collected above or below the 100 and 20 valuespreviously described. Additional analysis as provided herein improvesdiscrimination, proper selection and improved analysis of theseidentified objects. This additional level of analysis is unique in thatit is focused on identified objects and involves the use of spectralanalysis.

Contemplated methods of identifying wavelength(s) of light allow for animproved categorization of a cytological sample involve scanning asample throughout a spectral region and determining if particularwavelength(s) within that region allow for improved categorization of asample parameter. The sample may be scanned at regular or irregularintervals throughout the spectral region, and then combined in differentways with an unmodified image and/or with one or more differentwavelength-specific images. One portion of the sample may also bescanned at regular or irregular intervals throughout the spectral regionwith each wavelength-specific portion being reviewed automatically or bythe user, thus creating multiple wavelength-specific images of the sameportion of the sample.

A variety of different sample parameters may be analyzed to determinetheir affect on the ability to more accurately categorize an imagedsample. In some embodiments, it may be desirable to identify the borderof the nucleus. The regularity of the shape of the nucleus can provideimportant clues as to the status of the imaged cell and an irregularityin the nuclear shape can indicate a pre-malignant status. Therefore animproved ability to identify the nucleus, for example, by increasingcontrast between the nucleus and cytoplasm, would yield an improvedmethod of automatically diagnosing the condition of the cells.

In some embodiments, imaging the nucleus of the cells includesdetermining the texture of the nucleus, its shape, the integrateddarkness, the average darkness or a combination thereof. Texture refersto analyzing the value of a given pixel in comparison with neighboringpixels, as known in the art. Shape can be determined through anysuitable technique, for example by determining the square of theperimeter divided by 4π times area. Additionally, the “ring” ofcytoplasm surrounding the nucleus may also be used. The optical densityof the cytoplasm in this ring may be subtracted digitally from the imageto provide for increased ability to measure the nucleus and can allowfor improved visualization in situations where the cytoplasm ofdifferent cells overlap each other in a sample.

Although the examples herein describe cytological samples stained by aPapanicolaou staining process, it should be understood that the methodsdescribed herein can be used in conjunction with samples stained byother suitable and/or conventional processes and/or materials.Contemplated staining methods include hematoxylin and eosin staining,Feulgen stain, DNA staining, stoichiometric staining, andcounterstaining. In some embodiments, the methods may include or utilizesamples which are not stained. Additionally, although the examplesdepict the use of the methods with regard to gynecological samplesobtained from pap smears, any suitable biological sample may similarlybe utilized in the methods described herein.

Where a combination is disclosed herein, it is to be understood thateach sub-combination of the elements of that combination is alsospecifically disclosed and is within the scope of the subject matter.Conversely, where different elements or groups of elements aredisclosed, combinations thereof are also disclosed. Where any element ofthe subject matter is disclosed as having a plurality of alternatives,examples of that subject matter in which each alternative is excludedsingly or in any combination with the other alternatives are also herebydisclosed; more than one element of contemplated subject matter can havesuch exclusions, and all combinations of elements having such exclusionsare hereby disclosed.

Unless defined otherwise or the context clearly dictates otherwise, alltechnical and scientific terms used herein have the same meaning ascommonly understood by one of ordinary skill in the art to which thisinvention belongs. Although any methods and materials similar orequivalent to those described herein can be used in the practice ortesting of the subject matter disclosed herein, the preferred methodsand materials are now described.

As mentioned earlier, methods, process and/or apparatus described hereincombine the ability of existing automated imaging systems, such as theTIS, with the additional capability to analyze an identified subset of“objects of interest” in a specimen sample in order to provide for theautomatic recognition of normal cells, abnormal cells, particulardisease-related conditions, or a combination thereof.

One automated imaging process, as described herein, comprises: a)obtaining digital images of objects in a biological sample; b) selectinga plurality of objects of interest from the digital images; c) obtainingmultiple images of the objects of interest at a plurality of differentwavelengths; d) combining one of said multiple images with acorresponding digital image to produce a combined image; and e)analyzing the combined image in order to characterize the biologicalsample.

In this contemplated embodiment, objects of interest are identified inthe sample, and then additional images of these objects are obtained byilluminating the objects with other spectral regions. The additionalimages may be combined by any mathematical means, such as, e.g.,additively, subtractively, and/or in a ratio in the combined image. Morethan two images may be combined. The combined images are then analyzedby a set of criteria as described herein, and the results are comparedto those obtained from single-wavelength illumination. In this manner,additional useful illumination wavelengths can be identified. Theadditional images at a plurality of wavelengths may be acquired at thesame time as the original image was acquired and then stored for laterpossible use. Or, objects may be relocated and new images at a pluralityof wavelengths may then be acquired.

Another contemplated automated imaging process, as described herein,comprises: a) obtaining digital images of objects in a biologicalsample; b) selecting at least one object of interest from the digitalimages; c) obtaining at least one image of the at least one object ofinterest at a plurality of different wavelengths to form a set ofmulti-wavelength images; d) analyzing the set of multi-wavelength imagesin order to characterize the biological sample.

In this additional contemplated embodiment, at least one image collectedat a plurality of different wavelengths is used to extract features fromthe images. For example a ratio of darkness in the red end of thespectrum divided by darkness in the blue end of the spectrum would beuseful in characterizing the image taken from the biological sample.This contemplated embodiment is designed to provide multipleperspectives on the same image or collection of images from a biologicalsample. In related embodiments, the user might take 4 or 5 images andfind that some weighted value of the pixels within the nuclei from thedifferent images give a result that may indicate abnormality versusnormalcy. This process would give a “spectral signature” of the imagesfrom the biological sample.

In some embodiments, the imager first identifies the specific subsetbased on the highest integrated optical density nuclei and the highestaverage optical density clusters. In an abnormal specimen the subsettypically includes abnormal objects and some “false alarms.” In a normalspecimen the subset typically includes normal objects and also some“false alarms.” The false alarms are due to the presence ofreactive/repair types of cells or artifacts such as overlapping nucleior normal objects with inherently low contrast between the nucleus andthe cytoplasm.

In some embodiments provided herein, the imager returns to theseidentified objects and applies additional analysis or analyses to bettersort true abnormal objects from reactive/repair type changes and/or from“false alarms.” The additional analysis can include spectral analysis ormarker detection, and can involve measurements taken from both thenucleus and cytoplasm of the cells.

In some embodiments disclosed herein, a spectral analysis is performedon a specific subset of objects, such as the top 2000 objects, the top1000 objects or less, such as the top 500 objects, the top 200 objectsor the top 120 objects. The number of objects chosen for the subset is afunction of such things as the computer memory, computer speed and theneed of the user to characterize the sample with increasing accuracy.Once the subset of objects is selected and stored, an analysis of thetop 120 images or objects from that subset can be selected based onsuitable criteria. So, for example, a subset of objects may contain 2000images taken at one wavelength. At another wavelength, 1000 images arecollected. During analysis, 120 images are pulled from each of the 2000image set and the 1000 image set.

In other embodiments, rather than returning to the subset of objects,images at a plurality of wavelengths may be stored at the time theinitial images are acquired. Then additional analyses may be performedon the subset of objects of interest without relocating the object.

Multiple wavelengths of light can be used to digitize black and whiteimages taken at a single or multiple wavelengths. The resulting “color”images may be more easily characterized than a single black and whiteimage. In some embodiments, a classification of the objects can then beattempted. Based upon the analysis of the identified objects, a decisioncan made to identify a specimen as normal without requiring anyadditional review by a human.

Spectral information can also be used to identify specific types ofcells. For example, in identifying a list of clusters it would bedesirable to identify endometrial cells, or endocervical cells. Inidentifying a list of single nuclei, identification of metaplastic orendocervical cells or other specific cell types can be useful to thecytologist. In both types of identification, a certain level ofabnormality can be determined through spectral analysis as providedherein. Such measurements can include nuclear and cytoplasmic measuresof morphology and spectral information.

Spectral information can also detect certain cellular changes associatedwith disease or other cellular changes. For example, HPV infection maycause a cellular change that results in a spectral change. This can bedetected by an imager, allowing the sample to be identified as beinginfected with HPV, without requiring a molecular assay.

Changes in cells due to the presence of disease or infection are oftendemonstrated by the presence of markers. For example, antibodies candetect the presence of an infection, for example an HPV or Chlamydiainfection. Other molecular markers, such as nucleic acid probes oraptamers, can also be used to indicate the presence of disease orinfection. In some embodiments, probes can be attached to a unique colorlabel that is not normally present in the stain being used, for examplea standard Pap stain. This label can comprise a certain absorptionspectrum, or it may fluoresce only when a certain wavelength of light isused for illumination. The color analysis and/or illumination of themarker can be done on the identified objects.

Overall, this approach provides subsequent analysis of a reduced numberof objects on the slide, which allows faster execution than can beobtained with a full slide analysis. It also allows for increasedsensitivity or specificity since the additional analysis is only appliedto objects that are already selected as suspicious due to perceivedchanges in a relevant property, for example nuclear density.

The TIS, for example, identifies the 100 objects (usually nuclei) withthe highest integrated optical density (IOD). In a system utilizing amethod provided herein, a spectral analysis can be made of some or allof those 100 identified objects. The spectral analysis can be used togive an indication of whether these are cell nuclei having spectralcharacteristics more similar to negative cell nuclei or to abnormal cellnuclei. Based upon this analysis a decision can be made that the slideis likely negative and no further human analysis may be required.

Other embodiments of automated spectral imaging methods includeautomated analysis of specimens for diagnosis, sorting, or selectingcells for additional analysis, for Pap tests, ductal lavage, lung, etc.;and improved segmentation analysis by combinations of images obtainedfrom two or more colors of illumination. Also, automated methods mayinclude steps involving multispectral unmixing, segmentation, and/orquantification of the images or objects of interest.

It is to be understood that terms such as “color(s),” “wavelength(s)”and “spectral region(s)” used herein can encompass both precisewavelengths with narrow bandwidths, for example as might be provided bya laser source, and somewhat broader bandwidths as may be provided, forexample, by the use of filters with a broad- or multi-band light source.Light emitting diode (LED) illumination can provide either narrow orsomewhat broader illumination, depending on the individual LED.

The sample, which also may be referred to as the specimen, that isanalyzed can be any source of biological material that can be obtainedfrom an organism directly or indirectly, including cells, tissue orfluid. Nonlimiting examples of the sample include blood, urine, semen,milk, sputum, mucus, plueral fluid, pelvic fluid, synovial fluid,ascites fluid, body cavity washes, eye brushing, skin scrapings, abuccal swab, a vaginal swab, a pap smear, a rectal swab, an aspirate, aneedle biopsy, a section of tissue obtained for example by surgery orautopsy, plasma, serum, spinal fluid, lymph fluid, the externalsecretions of the skin, respiratory, intestinal, and genitourinarytracts, tears, saliva, tumors, organs, a microbial culture, a virus, andsamples of in vitro cell culture constituents. The sample can be apositive control sample which is known to contain an object of interest.

The object of interest that may be selected by the automated device maybe any component of the sample that is desired to be detected.Non-limiting examples of the object include a polynucleotide, a protein,a peptide, a polysaccharide, mucopolysaccharide, proteoglycan, acarbohydrate, a lipid, a fat, a cell, a cell type, an organism, a virus,a structure, an antigen, an inorganic compound, or other molecule towhich a sensor can be obtained.

Exemplary molecular objects include HPV E2 protein, HPV E6 and E7proteins, HPV L1 capsid protein, p16INK4a, E-cadherin, N-cadherin, p53,GCDFP-15, Pericyclin, NuMA, carbonic anhydrase, matrixmetalloproteinases, nuclear matrix proteins, ferritin, aurora A,pericentrin, osteopontin, prostatin, insulin-like growth factor,fibroblast growth factor, BRCA1, BRCA2, mammoglobin, PSE, CEA, CA-125,CA 19-9, CA 15-3, somatostatin, synaptophysin, chromogranin,kallikriens, fibronectin, EGFR, K-ras, Her-2/neu, treponemal antigen,neuron-specific enolase, retinoblastoma protein, hepatitis C surfaceantigen, sexually transmitted disease markers including the outermembrane protein of Chlamydia trachomatis, cancer markers, and HIVgp120.

Where the object is a cell or cell component or product, the cell can beof any origin, including prokaryotic, eukaryotic, or archea. The cellmay be living or dead. If obtained from a multicellular organism, thecell may be of any cell type. The cell may be a cultured cell line or aprimary isolate, the cell may be mammalian, amphibian, reptilian, plant,yeast, bacterial, mycobacterial, spirochetal, or protozoan. The cell maybe human, murine, rat, hamster, chicken, quail, or dog. The cell may bea normal cell, a mutated cell, a genetically manipulated cell, a tumorcell, etc.

In one embodiment for performing the automated imaging methods describedherein, a device includes one or more light sources capable ofilluminating the specimen at multiple wavelengths of light. The devicealso includes one or more detectors capable of obtaining images of thespecimen at multiple wavelengths of illumination.

The device also includes a computer or other selection means capable ofselecting a subset of objects of interest from images obtained from thespecimen at a first wavelength. The device may select these objectsbased on any set of criteria, which may include one or multiple separateanalyses. Examples of such criteria are provided herein, includingaverage optical density, integrated optical density, shape, texture,etc. The device is capable of imaging the identified objects of interestat a second wavelength, and then combining these additional images withthe first image of the objects to produce a combined image, which canthen be subject to additional analyses to select a particular subset ofthe objects based on further criteria, which may be the same ordifferent criteria as performed initially.

Images can be added together or compared to one another by analogdevices or by digital devices. For example, two images may be addedtogether by turning on two colors of illumination simultaneously (i.e.from two different wavelength LED's) and adding the images in an analogprocess. In other embodiments, the images may be digitized and added orcompared.

EXAMPLES

The following examples are set forth so as to provide those of ordinaryskill in the art with a complete description of how to make and use thesubject matter described herein, and are not intended to limit the scopeof what is regarded as the invention. Efforts have been made to ensureaccuracy with respect to numbers used (e.g., amounts, temperature, etc.)but some experimental error and deviation should be accounted for.

Example 1 Screening for Multiple Wavelengths to Improve AutomatedImaging

An experiment was performed to determine if imaging a sample at multiplewavelengths could enhance the operation of the scene segmentation and/orfeature extraction operations on an automated imager. Some abnormalcells and many metaplastic cells have reduced nuclear contrast due tovery thick cytoplasms. Additionally, some staining systems producemultiple colors in a stained sample, and a single color of illuminationmay not be optimal for all cells in the sample. For example,Papanicolaou staining can produce cells with red, blue or greencytoplasms, and a single wavelength as used with some digital imagersmay not provide optimum imaging of such divergent cells. Therefore,overall improvement in contrast was used as one means to assesspotential methods for improving analysis.

A set of eleven microscope fields containing normal, abnormal andmetaplastic Papanicolaou-stained cells was digitized using 51 differentwavelengths using a Zeiss Axioskop microscope with a black and whitevideo camera. This was accomplished by placing a monochrometer (EG&Gmodel 585-22) between the light source and the microscope. Images werethen digitized at wavelengths between 450 and 700 nanometers, in stepsof 5 nanometers. Once the multiple wavelength images were digitized, analgorithm was explored to add combinations of two images together, andthen automatically determine contrast between cell nuclei andcytoplasms. Contrast was defined as the grey level difference of a 10×10pixel box within the nucleus compared to a 10×10 pixel box within thecytoplasm. A single wavelength, 570 nm, was chosen that gave optimalcontrast for most images.

Combinations of this image with the other wavelengths were analyzed todetermine the change in contrast from the 570 nm image, for a combinedimage (the two images were added together and then divided by 2). FIG. 1shows a scatterplot of the net change in contrast (diff3 on the x axis;diff3=the difference in grey values between nucleus and cytoplasm forthe dual wavelength image, minus the difference in grey values betweenthe nucleus and cytoplasm for the single wavelength image) for each ofeleven objects for all 51 wavelength combinations (y axis) with the 570nm image.

A range (between approximately 600 and 670) was identified wherecontrast was improved in the combined image for all objects, regardlessof cytoplasmic color or cell type. This demonstrates that contrast canbe improved combining images from multiple wavelengths.

Example 2 Multiple Wavelength Imaging Improves Analysis of DifficultSpecimens

In order to explore the potential of using multiple wavelength imaging,a series of abnormal cells and normal metaplastic cells from Pap stainedslides were digitized using a Zeiss Axioskop microscope with a black andwhite video camera using two wavelengths, 570 and 650 nm, selected basedon Example 1. Full images were digitized to allow “confusion” ofclusters, debris, blood, etc. The images were first analyzed using onlya single wavelength—the standard green illumination used in many Paptest imaging systems (570 nm). The images were then analyzed using acombination of the two wavelengths 570 and 650 nm.

Cells were then automatically segmented to find the nuclei. Thesegmentation algorithm works by automatically finding potential nuclei(dark objects), and then uses an iterative method based upon the greylevel histogram of the image. This method monitors changes in theminimum and maximum darkness values from a histogram of the grey levelswithin the current outline of the object. Many other segmentationmethods can be applied to locate nuclei, cytoplasms or other objects inan image. After segmentation, features of the nuclei are extracted and arejection of artifacts is done, based upon shape and texturemeasurements. In order to test the performance of the combined image, asimple listing of cells in order using the integrated optical density(“IOD”) of the cells. This feature is one of the more discriminatory ofall features measured on slides. However, difficulty has beenencountered with “large/dark” but normal “metaplastic” cells appearingin positions in the list among the abnormal cells.

Patient samples with “troublesome” metaplastic cells were run and the 40cells with the highest IOD were stored in a list. When only a singlewavelength was used, metaplastic nuclei were appeared in the list amonga set of abnormal nuclei characteristic of high grade squamousintraepithelial lesions, one at position 28 and more in positions 33through 40. Many abnormal nuclei were found in the list of 100 nucleiwith the highest integrated optical density.

When combined images from the two wavelengths were used to create thelist of cellular IOD values from the same patient samples, however, ofthe first 40 nuclei the one and only metaplastic nucleus appeared inposition 37. Now, more abnormal nuclei were shown in the first 40position in the list. Thus, the combination demonstrated an ability torank cells by providing fewer “false positive” nuclei in the top rankingand shows the usefulness of two color analysis with this very difficultproblem.

As a final check, the images were analyzed for clusters by comparingdata from matching clusters in the single wavelength and dual wavelengthcombined images. The data showed a significant improvement in thedifference in standard deviation of grey levels between the “salt andpepper” appearance of white blood cell clusters and the smootherclusters (less variation in pixel density) of abnormal cells. Thisfeature is important as it allows removal of “false alarms” due to whiteblood cell clusters. Without this discrimination, an imager may selectsome of the very numerous white blood cell clusters to show acytotechnologist instead of the less frequent abnormal clusters thatmight be on the same slide. These data clearly indicate that contrastwas improved, permitting better discrimination by the imager. Dualwavelength illumination allowed improved segmentation and classificationin a clinical application with cells from Pap test slides.

Thus, specific embodiments, methods of use and applications of animproved automated image analysis system have been disclosed. It shouldbe apparent, however, to those skilled in the art that many moremodifications besides those already described are possible withoutdeparting from the inventive concepts herein.

1. An automated imaging process, comprising: obtaining digital images ofobjects in a biological sample; selecting a plurality of objects ofinterest from the digital images; obtaining multiple images of theselected objects of interest at a plurality of different wavelengths;combining two or more of said multiple images to produce a combinedimage of the selected objects; and analyzing the combined image in orderto characterize the selected objects.
 2. The automated imaging processof claim 1, wherein the objects of interest comprise objects ofrelatively high optical density compared with other objects in thebiological sample.
 3. The automated imaging process of claim 1, whereinthe digital images are obtained from at least one wavelength in therange from ultraviolet to infrared.
 4. The automated imaging process ofclaim 3, wherein the digital images are obtained from at least onewavelength of less than about 670 nm.
 5. The automated imaging processof claim 1, wherein the plurality of different wavelengths are atuniform increments.
 6. The automated imaging process of claim 1, whereinthe objects of interest comprise cells.
 7. The automated imaging processof claim 1, wherein the process is used to determine a normal specimen.8. The automated imaging process of claim 1, wherein the process is usedto identify an infection or other disease.
 9. The automated imagingprocess of claim 1, wherein the characterization of the selected objectsincludes detection of a marker.
 10. The automated imaging process ofclaim 1, wherein the characterization of the selected objects comprisesdetection of a spectral signature of an abnormal or infected cell. 11.An automated imaging process, comprising: obtaining digital images ofobjects in a biological sample; selecting at least one object ofinterest from the digital images; obtaining at least one image of the atleast one selected object of interest at a plurality of differentwavelengths to form a set of multi-wavelength images; analyzing the setof multi-wavelength images in order to characterize the at least oneselected object.
 12. The automated imaging process of claim 11, whereinthe at least one object of interest comprises an object of relativelyhigh optical density relative to other objects in the biological sample.13. The automated imaging process of claim 11, wherein the digitalimages are obtained from at least one wavelength in the range fromultraviolet to infrared.
 14. The automated imaging process of claim 13,wherein the digital images are obtained from at least one wavelength ofless than about 670 nm.
 15. The automated imaging process of claim 11,wherein the plurality of different wavelengths are at uniformincrements.
 16. The automated imaging process of claim 11, wherein theat least one object of interest comprise cells.
 17. The automatedimaging process of claim 16, wherein the process is used to identify anucleus.
 18. The automated imaging process of claim 17, wherein theprocess is used to identify the density of the nucleus.
 19. Theautomated imaging process of claim 11, wherein the characterization ofthe selected object includes detection of a marker.
 20. The automatedimaging process of claim 11, wherein the process is used to determine anormal specimen.
 21. The automated imaging process of claim 11, whereinthe process is used to identify an infection or other disease.
 22. Theautomated imaging process of claim 11, wherein the characterization ofthe selected object comprises detection of a spectral signature of anabnormal or infected cell.